Machine Learning Technology Research Articles

An optimal global biochar application strategy based on matching biochar and soil properties to reduce global cropland greenhouse gas emissions: findings from a global meta-analysis and density functional theory calculation

Biochar has been extensively utilized to amend soil and mitigate greenhouse gas (GHG) emissions from croplands. However, the effectiveness of biochar application in reducing cropland GHG emissions remains uncertain due to variations in soil properties and environmental conditions across regions. In this study, the impact of biochar surface functional groups on soil GHG emissions was investigated using molecular model calculation. Machine learning (ML) technology was applied to predict the responses of soil GHG emissions and crop yields under different biochar feedstocks and application rates, aiming to determine the optimum biochar application strategies based on specific soil properties and environmental conditions on a global scale. The findings suggest that the functional groups play an essential role in determining biochar surface activity and the soil’s capacity for adsorbing GHGs. ML was an effective method in predicting the changes in soil GHG emissions and crop yield following biochar application. Moreover, poor-fertility soils exhibited greater changes in GHG emissions compared to fertile soil. Implementing an optimized global strategy for biochar application may result in a substantial reduction of 684.25 Tg year−1 CO2 equivalent (equivalent to 7.87% of global cropland GHG emissions) while simultaneously improving crop yields. This study improves our understanding of the interaction between biochar surface properties and soil GHG, confirming the potential of global biochar application strategies in mitigating cropland GHG emissions and addressing global climate degradation. Further research efforts are required to optimize such strategies.Graphical

A Comprehensive Review of Artificial Intelligence

This paper explores the literature, types, applications, and challenges of Artificial Intelligence, providing a comprehensive overview of its current state and future potential. It traces the development of AI from early symbolic systems to modern machine learning and deep learning technologies, highlighting different AI types such as narrow AI, general AI, and the speculative concept of superintelligence. Through a literature review of various research papers, this paper examines the foundational theories and cutting-edge advancements in AI, including neural networks, reinforcement learning, and hybrid systems. The applications of AI across diverse industries, such as healthcare, finance, and education, demonstrate its transformative impact, while ethical concerns surrounding bias, privacy, and job displacement present significant challenges. The paper concludes by addressing the need for responsible AI development to ensure its benefits align with societal values and contribute to a sustainable future.

Hardware automatic test scheme and intelligent analyze application based on machine learning model

Abstract Hardware testing has always been the core of hardware development, and improving the performance and efficiency of hardware testing is very important for hardware development. Because hardware quality management is insufficient, many large hardware tools were developed using manual workshop technology in the past and could hardly be maintained. This can lead to the cancellation of the project, causing major personnel and property losses. Improving hardware quality and ensuring security are very complex problems. Hardware testing is usually conducted through manual and automatic testing, and the limitations of manual testing are increasingly obvious. So hardware automatic testing technology has attracted people’s attention in recent years. It has become an important research direction in the field hardware testing and can overcome many problems of traditional testing methods. Strict test rules, based on standards and scores, provide a fully automated test process. With the continuous improvement of network technology, the functions and scope of hardware are constantly enriched and expanded. With the acceleration of hardware updates and development, this has brought a heavy burden to the previous hardware testing work. The purpose of this article was to study the application of machine learning technology in the field of hardware automatic testing and provide an appropriate theoretical basis for optimizing testing methods. This article introduced the research methods of hardware automatic testing technology, introduced three automatic testing framework models, and summarized the application of machine learning in hardware testing. It included hardware security and reliability analysis, hardware defect prediction, and source-based research. Then, this article studied the defect prediction model and machine learning algorithm and constructed a hardware defect prediction model based on machine learning based on the theory. First, the data were preprocessed, and then, the Stacking method was used to build a comprehensive prediction model, and four prediction results evaluation indicators were established. In the experiment part, the defect prediction results of the hardware automatic test model were studied. The results showed that the hardware defect prediction model based on machine learning had higher accuracy, recall rate, F_measure and area under curve. Compared with other models, the average accuracy of the hardware defect prediction model in this article was 0.092 higher, which was more suitable for automatic hardware testing and analysis.

Exploring Technological Innovation in Wave Forecasting Using Machine Learning: A Literature Analysis

In the face of rapid technological advancements, innovations in wave forecasting are increasingly essential for effectively addressing the complex impacts of climate change. This study aims to explore technological developments in wave forecasting that can manage the complexities related to climate change and enhance the accuracy and efficiency of predictions in dynamic marine environments. Employing a qualitative approach through a Systematic Literature Review (SLR) methodology, the research focuses on literature from databases such as Scopus, DOAJ, and Google Scholar, specifically targeting publications from 2014 to 2024. Recent findings reveal that advancements in machine learning technologies, including deep learning, ensemble learning, transfer learning, and data augmentation, have significantly improved the precision and efficiency of wave forecasting models. Techniques like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have been particularly effective in capturing complex, non-linear patterns within wave data, enhancing the overall prediction accuracy. Ensemble learning methods have further contributed by increasing the stability and robustness of forecasts. Moreover, transfer learning and data augmentation play vital roles in adapting these models to rapidly changing environmental conditions, making them highly relevant in the context of climate change. These approaches are crucial for models to remain adaptable and responsive to dynamic oceanic conditions influenced by climate variability. The insights derived from this study are expected to provide valuable direction for the future development of machine learning-based wave forecasting models, emphasizing the need for innovative techniques that can accommodate the complexities and uncertainties brought about by climate change.

Advancements in Technologies and Methodologies of Machine Learning in Landslide Susceptibility Research: Current Trends and Future Directions

Landslides are pervasive geological hazards that pose significant risks to human life, property, and the environment. Understanding landslide susceptibility is crucial for predicting and mitigating these disasters. This article advocates for a comprehensive review by systematically compiling and analyzing 146 relevant studies up to 2024. It assesses current progress and limitations and offers guidance for future research. This paper provides a comprehensive overview of the diverse challenges encountered by machine learning models in landslide susceptibility assessment, encompassing aspects such as model selection, the formulation of evaluation index systems, model interpretability, and spatial heterogeneity. The construction of an evaluation index system, which serves as the foundational data for the model, profoundly influences its accuracy. This study extensively investigates the selection of evaluation factors and the identification of positive and negative samples, proposing valuable methodologies. Furthermore, this paper briefly deliberates and compares classical machine learning models, offering valuable insights for model selection. Additionally, it delves into discussions concerning model interpretability and spatial heterogeneity issues. These research findings promise to enhance the precision of landslide susceptibility assessments and furnish effective strategies for risk management.

Hybrid wind speed optimization forecasting system based on linear and nonlinear deep neural network structure and data preprocessing fusion

Wind speed time series forecasting has been widely used in wind power generation. However, the nonlinear and non-stationary characteristics of wind speed make accurate wind speed forecasting a difficult task. In recent years, the rapid development of artificial intelligence and machine learning technology provides a new solution to the problem of wind speed forecasting. Combining the advance of artificial intelligence and data analysis strategy, this paper proposes a wind speed forecasting system based on multi-model fusion and integrated learning. Considering the differences in data observation and training principles of various algorithms and exploiting the advantages of each model, a wind speed forecasting system with multiple machine learning algorithms embedded in integrated learning is constructed, which includes three modules: data preprocessing, optimization and forecasting. The data preprocessing module can conduct quantitative analysis through input data decomposition and feature extraction, and the combination of multi-objective intelligent optimization algorithm and combined forecasting method can effectively forecast the wind speed time series. The validity of the algorithm is verified using the data of Shandong wind farm in China. The forecasting results show that compared with the traditional single model forecasting, the proposed integrated wind speed forecasting system based on the fusion of multiple models has higher forecasting accuracy.

Dynamic robustness evaluation for automated model selection in operation

Context:The increasing use of artificial neural network (ANN) classifiers in systems, especially safety-critical systems (SCSs), requires ensuring their robustness against out-of-distribution (OOD) shifts in operation, which are changes in the underlying data distribution from the data training the classifier. However, measuring the robustness of classifiers in operation with only unlabeled data is challenging. Additionally, machine learning engineers may need to compare different models or versions of the same model and switch to an optimal version based on their robustness. Objective:This paper explores the problem of dynamic robustness evaluation for automated model selection. We aim to find efficient and effective metrics for evaluating and comparing the robustness of multiple ANN classifiers using unlabeled operational data. Methods:To quantitatively measure the differences between the model outputs and assess robustness under OOD shifts using unlabeled data, we choose distance-based metrics. An empirical comparison of five such metrics, suitable for higher-dimensional data like images, is performed. The selected metrics include Wasserstein distance (WD), maximum mean discrepancy (MMD), Hellinger distance (HL), Kolmogorov–Smirnov statistic (KS), and Kullback–Leibler divergence (KL), known for their efficacy in quantifying distribution differences. We evaluate these metrics on 20 state-of-the-art models (ten CIFAR10-based models, five CIFAR100-based models, and five ImageNet-based models) from a widely used robustness benchmark (RobustBench) using data perturbed with various types and magnitudes of corruptions to mimic real-world OOD shifts. Results:Our findings reveal that the WD metric outperforms others when ranking multiple ANN models for CIFAR10- and CIFAR100-based models, while the KS metric demonstrates superior performance for ImageNet-based models. MMD can be used as a reliable second option for both datasets. Conclusion:This study highlights the effectiveness of distance-based metrics in ranking models’ robustness for automated model selection. It also emphasizes the significance of advancing research in dynamic robustness evaluation.

Progress in Remote Sensing of Heavy Metals in Water

This review article details the advancements in detecting heavy metals in aquatic environments using remote sensing methodologies. Heavy metals are significant pollutants in aquatic environment, and their detection and monitoring are crucial for predicting water quality. Traditional in situ water sampling methods are time-consuming and costly, highlighting the advantages of remote sensing techniques. Analysis of the reflectance and absorption characteristics of heavy metals has identified the red and near-infrared bands as the sensitive wavelengths for heavy metal detection in aquatic environments. Several studies have demonstrated a correlation between total suspended matter and heavy metals, which forms the basis for retrieving heavy metal content from TSM data. Recent developments in hyperspectral remote sensing and machine (deep) learning technologies may pave the way for developing more effective heavy metal detection algorithms.

Applications of Machine Learning Technologies for Feedstock Yield Estimation of Ethanol Production

Biofuel has received worldwide attention as one of the most promising renewable energy sources. Particularly, in many countries such as the U.S. and Brazil, first-generation ethanol from corn and sugar cane has been used as automobile fuel after blending with gasoline. Nevertheless, in order to continuously increase the use of biofuels, efforts are needed to reduce the cost of biofuel production and increase its profitability. This can be achieved by increasing the efficiency of a sequential biofuel production process consisting of multiple operations such as feedstock supply, pretreatment, fermentation, distillation, and biofuel transportation. This study aims at investigating methodologies for predicting feedstock yields, which is the earliest step for stable and sustainable biofuel production. Particularly, this study reviews feedstock yield estimation approaches using machine learning technologies that focus on gradually improving estimation accuracy by using big data and computer algorithms from traditional statistical approaches. Given that it is becoming increasingly difficult to stably produce biofuel feedstocks as climate change worsens, research on developing predictive modeling for raw material supply using the latest ML techniques is very important. As a result, this study will help researchers and engineers predict feedstock yields using various machine learning techniques, and contribute to efficient and stable biofuel production and supply chain design based on accurate predictions of feedstocks.

Towards real-time myocardial infarction diagnosis: a convergence of machine learning and ion-exchange membrane technologies leveraging miRNA signatures.

Rapid diagnosis of acute myocardial infarction (AMI) is crucial for optimal patient management. Accurate diagnosis and time of onset of an acute event can influence treatment plans, such as percutaneous coronary intervention (PCI). PCI is most beneficial within 3 hours of AMI onset. MicroRNAs (miRNAs) are promising biomarkers, with potential of early AMI diagnosis, since they are released before cell death and subsequent release of larger molecules [e.g., cardiac troponins (cTn)], and have greater sensitivity and stability in plasma versus cTn regardless of timing of AMI onset. However, miRNA-based AMI diagnosis can result in false positives due to miRNA content overlap between AMI and stable coronary artery disease (CAD). Accordingly, we explored the possibility of using a miRNA profile, rather than a single miRNA, to distinguish between CAD and AMI, as well as different stages following AMI onset. First we screened a library of 800 miRNA using plasma samples from 4 patient cohorts; no known CAD, CAD, ST-segment elevation myocardial infarction (STEMI) and STEMI followed by PCI, using Nanostring miRNA profiling technology. From this screening, based on machine learning SCAD and Lasso algorithms, we identified 9 biomarkers (miR-200b, miR-543, miR-331, miR-3605, miR-301a, miR-18a, miR-423, miR-142, and miR-132) that were differentially expressed in CAD, STEMI and STEMI-PCI and explored them to identify a miRNA profile for rapid and accurate AMI diagnosis. These 9 miRNAs were selected as the most frequently identified targets by SCAD and Lasso, as indicated in the "drum-plot" model in the machine learning approach. We used age-matched patient samples to validate selected 9 miRNA biomarkers using a multiplexed ion-exchange membrane-based miRNA sensor platform, which measures specific miRNAs, and cTn as a control, simultaneously as a point-of-care device. Findings from this study will inform timely and accurate diagnosis of AMI and its stages, which are essential for effective management and optimal patient outcomes.

Optical dromions for M-fractional Kuralay equation via complete discrimination system approach along with sensitivity analysis and quasi-periodic behavior

This paper investigates new analytical wave solutions for the fractional Kuralay-IIB equations (FK-IIBE), including a new definition of the fractional derivative. This model works in disciplines such as ferromagnetic materials, optical fibers, wave mixing, and nonlinear optics. The integrable motion of space curves can be determined by our governing model. This research holds great importance in the area of optical fibers, exact and analytical solitons solution. The polynomial method’s complete discrimination system (CDSPM) is used to identify a variety of exact and analytical soliton solutions such as periodic, hyperbolic, rational, Jacobian elliptic (JE), and trigonometric functions. Additionally, we also convert the JE function into a solitary wave (SW) solution. To provide a comprehensive understanding of the dynamic aspects of the system, we also address bifurcation points, quasi-periodic behavior, sensitivity analysis, Poincarè maps, time series profile, and critical solution conditions. The research offers straightforward, efficient algorithms that can consistently and effectively address FK-IIBE. Machine learning technologies are also utilized to satisfy the criteria defined by dynamic analysis. Python regression learner tools are used for reverse engineering to analyze the mathematical model’s equilibrium based on parameter intervals and thresholds determined by dynamical analysis.

A Machine Learning Assisted Non-Enzymatic Electrochemical Biosensor to Detect Urea Based on Multi-Walled Carbon Nanotube Functionalized with Copper Oxide Micro-Flowers.

Detecting urea is crucial for diagnosing related health conditions and ensuring timely medical intervention. The addition of machine learning (ML) technologies has completely changed the field of biochemical sensing, providing enhanced accuracy and reliability. In the present work, an ML-assisted screen-printed, flexible, electrochemical, non-enzymatic biosensor was proposed to quantify urea concentrations. For the detection of urea, the biosensor was modified with a multi-walled carbon nanotube-zinc oxide (MWCNT-ZnO) nanocomposite functionalized with copper oxide (CuO) micro-flowers (MFs). Further, the CuO-MFs were synthesized using a standard sol-gel approach, and the obtained particles were subjected to various characterization techniques, including X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), and Fourier transform infrared (FTIR) spectroscopy. The sensor's performance for urea detection was evaluated by assessing the dependence of peak currents on analyte concentration using cyclic voltammetry (CV) at different scan rates of 50, 75, and 100 mV/s. The designed non-enzymatic biosensor showed an acceptable linear range of operation of 0.5-8 mM, and the limit of detection (LoD) observed was 78.479 nM, which is well aligned with the urea concentration found in human blood and exhibits a good sensitivity of 117.98 mA mM-1 cm-2. Additionally, different regression-based ML models were applied to determine CV parameters to predict urea concentrations experimentally. ML significantly improves the accuracy and reliability of screen-printed biosensors, enabling accurate predictions of urea levels. Finally, the combination of ML and biosensor design emphasizes not only the high sensitivity and accuracy of the sensor but also its potential for complex non-enzymatic urea detection applications. Future advancements in accurate biochemical sensing technologies are made possible by this strong and dependable methodology.

Omnicare: A Comprehensive ML/DL-Based Prediction System for Healthcare and Agriculture

Effective decision-making in healthcare and agriculture can be challenging due to the complexity and volume of data involved. Traditional methods often require extensive manual analysis and domain-specific expertise, which can be time-consuming and prone to error. The challenge is compounded by the need for real-time insights and accurate predictions to address critical issues such as disease diagnosis and crop management. Omnicare tackles these challenges by using advanced machine learning and deep learning technologies to facilitate predictions. With pretrained models, it delivers precise insights for medical and agricultural needs, eliminating the need for user-provided training data. This streamlined, user-friendly approach enhances decision-making and represents a significant advancement in applying AI to real-world problems

Trends in South Korean Medical Device Development for Attention-Deficit/Hyperactivity Disorder and Autism Spectrum Disorder: Narrative Review.

Attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are among the most prevalent mental disorders among school-aged youth in South Korea and may play a role in the increasing pressures on teachers and school-based special education programming. A lack of support for special education; tensions between teachers, students, and parents; and limited backup for teacher absences are common complaints among Korean educators. New innovations in technology to screen and treat ADHD and ASD may offer relief to students, parents, and teachers through earlier and efficient diagnosis; access to treatment options; and ultimately, better-managed care and expectations. This narrative literature review provides an account of medical device use and development in South Korea for the diagnosis and management of ADHD and ASD and highlights research gaps. A narrative review was conducted across 4 databases (PubMed, Korean National Assembly Library, Scopus, and PsycINFO). Journal articles, dissertations, and government research and development reports were included if they discussed medical devices for ADHD and ASD. Only Korean or English papers were included. Resources were excluded if they did not correspond to the research objective or did not discuss at least 1 topic about medical devices for ADHD and ASD. Journal articles were excluded if they were not peer reviewed. Resources were limited to publications between 2013 and July 22, 2024. A total of 1794 records about trends in Korean medical device development were categorized into 2 major groups: digital therapeutics and traditional therapy. Digital therapeutics resulted in 5 subgroups: virtual reality and artificial intelligence, machine learning and robot, gaming and visual contents, eye-feedback and movement intervention, and electroencephalography and neurofeedback. Traditional therapy resulted in 3 subgroups: cognitive behavioral therapy and working memory; diagnosis and rating scale; and musical, literary therapy, and mindfulness-based stress reduction. Digital therapeutics using artificial intelligence, machine learning, and electroencephalography technologies account for the biggest portions of development in South Korea, rather than traditional therapies. Most resources, 94.15% (1689/1794), were from the Korean National Assembly Library. Limitations include small sizes of populations to conclude findings in many articles, a lower number of articles discussing medical devices for ASD, and a majority of articles being dissertations. Emerging digital medical devices and those integrated with traditional therapies are important solutions to reducing the prevalence rates of ADHD and ASD in South Korea by promoting early diagnosis and intervention. Furthermore, their application will relieve pressures on teachers and school-based special education programming by providing direct supporting resources to students with ADHD or ASD. Future development of medical devices for ADHD and ASD is predicted to heavily rely on digital technologies, such as those that sense people's behaviors, eye movement, and brainwaves.

Image Analysis in Histopathology and Cytopathology: From Early Days to Current Perspectives.

Both pathology and cytopathology still rely on recognizing microscopical morphologic features, and image analysis plays a crucial role, enabling the identification, categorization, and characterization of different tissue types, cell populations, and disease states within microscopic images. Historically, manual methods have been the primary approach, relying on expert knowledge and experience of pathologists to interpret microscopic tissue samples. Early image analysis methods were often constrained by computational power and the complexity of biological samples. The advent of computers and digital imaging technologies challenged the exclusivity of human eye vision and brain computational skills, transforming the diagnostic process in these fields. The increasing digitization of pathological images has led to the application of more objective and efficient computer-aided analysis techniques. Significant advancements were brought about by the integration of digital pathology, machine learning, and advanced imaging technologies. The continuous progress in machine learning and the increasing availability of digital pathology data offer exciting opportunities for the future. Furthermore, artificial intelligence has revolutionized this field, enabling predictive models that assist in diagnostic decision making. The future of pathology and cytopathology is predicted to be marked by advancements in computer-aided image analysis. The future of image analysis is promising, and the increasing availability of digital pathology data will invariably lead to enhanced diagnostic accuracy and improved prognostic predictions that shape personalized treatment strategies, ultimately leading to better patient outcomes.

Research on Medical Device Software Based on Artificial Intelligence and Machine Learning Technologies

With the rapid development of Artificial Intelligence (AI) and Machine Learning (ML) technologies, new opportunities for innovation and application in medical device software have emerged. This paper explores the current research on medical device software based on AI and ML, analyzing the practical applications and outcomes of these technologies in the medical field. First, the basic concepts of AI and ML and their significance in medical devices are introduced. Then, an in-depth analysis of the functional requirements of medical device software, user experience, and relevant regulations is provided. Next, the paper discusses the specific applications of AI and ML in data processing, model training, and software implementation, showcasing their practical effects through case studies. Finally, the paper summarizes the current technical challenges and future opportunities, hoping to provide insights for further development in the medical device industry.

Multi-fidelity data mining-based design optimization framework for scramjet-based aircraft

Aircraft design faces complex multidisciplinary problems and often involves numerous design variables, making direct optimization a time-consuming task. With the development of big data and machine learning technologies, using data mining methods to assist optimization has become key to addressing the inefficiency in multidisciplinary design optimization (MDO). To address the high cost of data acquisition in current data mining, this paper proposes an aircraft design optimization framework based on multi-fidelity data mining, using a large amount of inexpensive cursory estimation data to execute the MDO process and form a multidisciplinary optimization database. Then, data mining methods are used to decouple disciplinary relationships, transforming multidisciplinary coupling problems into simpler multi-objective problems. Subsequently, under viscous CFD computation conditions, the EDGE-p-BFFD deformation method is used to fine-tune the obtained configurations, further enhancing multidisciplinary performance. Before both optimizations, data mining methods were used to analyze the impact of design variables on objective functions, forming design knowledge and completing variable selection. In the multidisciplinary optimization practice of airbreathing aircraft, it can be found that the proposed framework effectively achieves disciplinary decoupling and explores the relationship between shape and performance. In the multidisciplinary optimization of the waverider based on cursory estimations, particle swarm optimization increased the aircraft's cruising range by 8.74 %. In the multi-objective optimization of the lifting body based on viscous CFD calculations, data mining was used to transform the problem into several key objectives. The MOEA/D algorithm increased the range by 2.9 %, validating the effectiveness of the data mining-based MDO framework proposed in this paper.

The method of threat assessment and interference distribution within the framework of cognitive electronic warfare

Problem statement. Electromagnetic spectrum control (EMC) plays an important role in conflict radar. The EMS operational area has unique characteristics that can be used to gain superiority over the enemy. The use of advanced developments in cognitive electronic warfare (CRAB) makes it possible to achieve a dominant position in the use of EMS. The combination of artificial intelligence and machine learning technologies with advanced electronic warfare (EW) technologies has made it possible to take an important step in ensuring maneuverable operations in a duel situation aircraft (LA) – radar station (radar). The presented approach demonstrates the increased efficiency of the CRE due to the use of the threat assessment and interference distribution method (SDR). Goal. The purpose of the article is to show how modern technologies provide a noise distribution strategy that allows aircraft to survive in a dueling situation, despite its complexity. Results. It is shown that the ESD method uses various radar jamming modes, such as: interaction between types and sources of jamming, relative frequency and bandwidth, uncertainty of the threatening environment, and also models the evaluation of radar effectiveness from search to guidance. Practical significance. The article confirms that the conflict radar scenario, which takes into account threat assessments for aircraft and determines the distribution of interference for radar, allows us to determine the optimal interference strategy.

An overview of current developments and methods for identifying diabetic foot ulcers: A survey

AbstractDiabetic foot ulcers (DFUs) present a substantial health risk across diverse age groups, creating challenges for healthcare professionals in the accurate classification and grading. DFU plays a crucial role in automated health monitoring and diagnosis systems, where the integration of medical imaging, computer vision, statistical analysis, and gait information is essential for comprehensive understanding and effective management. Diagnosing DFU is imperative, as it plays a major role in the processes of diagnosis, treatment planning, and neuropathy research within automated health monitoring and diagnosis systems. To address this, various machine learning and deep learning‐based methodologies have emerged in the literature to support healthcare practitioners in achieving improved diagnostic analyses for DFU. This survey paper investigates various diagnostic methodologies for DFU, spanning traditional statistical approaches to cutting‐edge deep learning techniques. It systematically reviews key stages involved in diabetic foot ulcer classification (DFUC) methods, including preprocessing, feature extraction, and classification, explaining their benefits and drawbacks. The investigation extends to exploring state‐of‐the‐art convolutional neural network models tailored for DFUC, involving extensive experiments with data augmentation and transfer learning methods. The overview also outlines datasets commonly employed for evaluating DFUC methodologies. Recognizing that neuropathy and reduced blood flow in the lower limbs might be caused by atherosclerotic blood vessels, this paper provides recommendations to researchers and practitioners involved in routine medical therapy to prevent substantial complications. Apart from reviewing prior literature, this survey aims to influence the future of DFU diagnostics by outlining prospective research directions, particularly in the domains of personalized and intelligent healthcare. Finally, this overview is to contribute to the continual evolution of DFU diagnosis in order to provide more effective and customized medical care.This article is categorized under: Application Areas > Health Care Technologies > Machine Learning Technologies > Artificial Intelligence

Review and Analysis of Mathematical Approaches for Adaptive Authentication using Machine Learning

In today’s technological era, the combined problem of assuring user productivity and maintaining strong security standards has become quite a key challenge. As security considerations, have not to hurt productivity, significant efforts must be taken to achieve such goals. The notion of adaptive authentication comes into play to avoid friction in user experience so that a balance between security and user productivity can be maintained. Traditional authentication frameworks are becoming more obsolete due to their ineffectiveness in incorporating additional risk criteria such as location, network type, and operating system into the authentication management process. Adaptive authentication is a combination of authentication and machine learning (ML) technologies that can help in realizing secure intelligent systems. The goal of this research is to provide a complete review of the current applications of various machine learning algorithms often employed in adaptive authentication techniques. Throughout this study, the advantages, limits, and suggestions for future research will be thoroughly discussed.